[www] [BibTex]

Note: inproceedings

Abstract: Training segmentation models for CT scans in the absence of input data is a challenging problem. Methods based on generative adversarial networks translate images from other modalities but still require additional data and training. Synthesizing images directly from segmentation masks using heuristics can overcome this limitation. However, capabilities for model generalization remain underexplored for these methods. In this study, we generate synthetic data for liver segmentation using organ labels and prior CT knowledge. Ground truth labels serve as a source of information about global structures and are filled with artificial textures in various settings. Segmentation models trained on synthetic data demonstrate sufficient generalization to real CT data, highlighting a perspective of a simple yet powerful approach to data bootstrapping.

[www]

Note: inproceedings

Abstract: Training segmentation models for CT scans in the absence of input data is a challenging problem. Methods based on generative adversarial networks translate images from other modalities but still require additional data and training. Synthesizing images directly from segmentation masks using heuristics can overcome this limitation. However, capabilities for model generalization remain underexplored for these methods. In this study, we generate synthetic data for liver segmentation using organ labels and prior CT knowledge. Ground truth labels serve as a source of information about global structures and are filled with artificial textures in various settings. Segmentation models trained on synthetic data demonstrate sufficient generalization to real CT data, highlighting a perspective of a simple yet powerful approach to data bootstrapping.

[www] [BibTex]

Note: inproceedings

Abstract: Training segmentation models for CT scans in the absence of input data is a challenging problem. Methods based on generative adversarial networks translate images from other modalities but still require additional data and training. Synthesizing images directly from segmentation masks using heuristics can overcome this limitation. However, capabilities for model generalization remain underexplored for these methods. In this study, we generate synthetic data for liver segmentation using organ labels and prior CT knowledge. Ground truth labels serve as a source of information about global structures and are filled with artificial textures in various settings. Segmentation models trained on synthetic data demonstrate sufficient generalization to real CT data, highlighting a perspective of a simple yet powerful approach to data bootstrapping.

[www]

Note: inproceedings

Abstract: Training segmentation models for CT scans in the absence of input data is a challenging problem. Methods based on generative adversarial networks translate images from other modalities but still require additional data and training. Synthesizing images directly from segmentation masks using heuristics can overcome this limitation. However, capabilities for model generalization remain underexplored for these methods. In this study, we generate synthetic data for liver segmentation using organ labels and prior CT knowledge. Ground truth labels serve as a source of information about global structures and are filled with artificial textures in various settings. Segmentation models trained on synthetic data demonstrate sufficient generalization to real CT data, highlighting a perspective of a simple yet powerful approach to data bootstrapping.

[www]

Note: inproceedings

Abstract: Training segmentation models for CT scans in the absence of input data is a challenging problem. Methods based on generative adversarial networks translate images from other modalities but still require additional data and training. Synthesizing images directly from segmentation masks using heuristics can overcome this limitation. However, capabilities for model generalization remain underexplored for these methods. In this study, we generate synthetic data for liver segmentation using organ labels and prior CT knowledge. Ground truth labels serve as a source of information about global structures and are filled with artificial textures in various settings. Segmentation models trained on synthetic data demonstrate sufficient generalization to real CT data, highlighting a perspective of a simple yet powerful approach to data bootstrapping.

[www] [BibTex]

Note: inproceedings

Abstract: Training segmentation models for CT scans in the absence of input data is a challenging problem. Methods based on generative adversarial networks translate images from other modalities but still require additional data and training. Synthesizing images directly from segmentation masks using heuristics can overcome this limitation. However, capabilities for model generalization remain underexplored for these methods. In this study, we generate synthetic data for liver segmentation using organ labels and prior CT knowledge. Ground truth labels serve as a source of information about global structures and are filled with artificial textures in various settings. Segmentation models trained on synthetic data demonstrate sufficient generalization to real CT data, highlighting a perspective of a simple yet powerful approach to data bootstrapping.